Neural Surrogate Forward Modelling For Electrocardiology Without Explicit Intracellular Conductivity Tensor
For clinicians and researchers in cardiac electrophysiology, this approach reduces structural uncertainty in non-invasive assessment of atrial fibrillation.
This paper introduces a deep learning surrogate for cardiac forward modelling that maps intracellular potentials to ECGs without requiring explicit intracellular conductivity tensors, achieving an R2 of 0.949 ± 0.037 on 74 subjects.
Accurate forward modelling is essential for non-invasive cardiac electrophysiology, particularly in atrial fibrillation, where electrical activation is highly disorganised. Conventional physics-based forward models require explicit specification of intracellular conductivity tensors, which are not directly measurable in clinical practice and introduce structural modelling errors. This proof-of-concept study presents a deep learning approach that learns a direct mapping from left atrial intracellular electrical potentials to far-field ECGs without requiring explicit intracellular conductivity inputs at inference time. Despite training only on 74 subjects, the model achieved an R2 of 0.949 \pm 0.037, highlighting potential to reduce structural uncertainty and improve non-invasive AF assessment.